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This course introduces EEG-based Brain-computer Interfaces
(BCIs) from end to end: quality control, preprocessing
(filtering, artifact handling/ICA), epoching, feature
extraction, decoding, evaluation (with strong emphasis on
avoiding data leakage), and decision logic for
feedback/control. Students implement and evaluate at least
two BCI paradigms, including P300 and SSVEP (with motor
imagery as an additional paradigm), and connect design
choices to latency, accuracy, and robustness in both offline
and streaming settings. The course builds the necessary
machine learning and signal processing tools that are
commonly used in BCIs.
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